Image enhancement method and system for fiducial-less tracking of treatment targets

ABSTRACT

A method and system are presented for enhancing one or more images of an object, so as to increase the visibility within the images of one or more structures within the object. The object may be an anatomical region of a patient, and may include one or more reference structures, for example skeletal structures or vertebral structures, and one or more treatment targets, for example tumors or lesions. An operator, for example a top-hat filter operator, selects at least a first neighborhood and a second neighborhood within the images. The operator selects within each neighborhood one or more pixels having an optimal pixel value, and eliminates the remaining pixels in these neighborhoods. When the operator is applied to the selected neighborhoods, only the pixels having the greatest pixel values remain in the selected neighborhoods, and the remaining pixels are eliminated in the selected neighborhoods. As a result, desired features can be located and enhanced in the images.

REFERENCE TO RELATED APPLICATION

This application is a divisional of application Ser. No. 10/881,208,filed Jun. 30, 2004.

TECHNICAL FIELD

Embodiments of the present invention relate to the field of imageenhancement.

BACKGROUND

In image registration, an optimal transformation is sought betweendifferent image acquisitions of one or more objects. Image registrationtechniques may be used in the medical field to relate a pre-operativeimage of a patient's anatomy to a near real time image of the patientduring actual treatment. During radiosurgery, for example, the change intarget position at the time of treatment, as compared to its position atthe time of the diagnostic treatment planning, may be detected. This maybe accomplished by registering the 2D image acquired at treatment timewith the 3D scan obtained at the time of treatment planning. A robustand accurate 2D-3D image registration algorithm may enable the positionof the target, as viewed in the real-time 2D image, to be properlycorrelated with the pre-operative 3D scan. In practice, a formalmathematical transformation may be derived that best aligns thepre-operative image coordinate system with the patient's physical worldcoordinate system, defined for example in the treatment room.

Fiducial markers may be attached to, or implanted within, the patientbefore the pre-operative images are acquired, in order to accomplish apoint-based alignment of the different coordinate systems. Thesefiducial markers are typically designed so that they can be localizedrelatively accurately in the pre-operative image, as well as in the realphysical world. The respective localization points may then be used tocalculate a rigid body transformation between the two coordinatesystems.

Fiducials tracking can be difficult for the patient, however, for anumber of reasons. For example, high accuracy tends to be achieved byusing bone-implanted fiducial markers, but less invasive techniques suchas skin-attached markers or anatomical positions tend to be lessaccurate. Implantation of fiducials into a patient may be painful anddifficult, especially for the C-spine, the implantation process forwhich may frequently lead to clinical complications. Attempts havetherefore been

made to develop techniques for fiducial-less tracking.

By using anatomical structures, for example skeletal or vertebralstructures, as reference points, the need for fiducial markers (andensuing surgical implantation) may be eliminated in image-guidedsurgery.

While fiducial-less tracking that relies on skeletal structures mayovercome many of the drawbacks associated with implanted fiducials,these skeletal structures may frequently not be easily visible, or mayeven be hidden, in the pre-operative images, as well as in the real-timeprojection images.

Accordingly, there is a need for image enhancement techniques that canincrease the contrast and bring out the details of these referencestructures in image-guided surgery, both in the pre-operative images aswell as in the real-time projection images.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A provides an overall schematic block diagram of a fiducial-lesstracking method and system.

FIG. 1B illustrates the geometric relations between a three-dimensionaltreatment target and two orthogonal 2D x-ray projections of the target.

FIG. 2 illustrates a schematic flowchart of a non-rigid imageregistration algorithm used in one embodiment.

FIG. 3A schematically illustrates the generation of 2D DRRs from 3D CTscan data of an anatomical region that includes at least one treatmenttarget and at least one reference structure.

FIG. 3B is a schematic plot of a non-linear x-ray attenuation model formodifying CT numbers, in order to generate improved quality DRRs.

FIG. 4 illustrates exemplary images that have been enhanced to increasethe visibility of skeletal structures, using top hat filtering.

FIGS. 5A and 5B schematically illustrate local motion estimation for agiven point of interest within a target in a patient, using blockmatching.

FIG. 6 schematically illustrates multi-level block matching, in oneembodiment.

FIG. 7 schematically illustrates a neighborhood R for calculating asimilarity measure based on pattern intensity.

FIGS. 8A and 8B provide plots of the similarity measure functions usedfor the local motion estimation illustrated in FIGS. 5A and 5B,respectively. In FIGS. 8A and 8B, the similarity measure functions areplotted with respect to translations in two mutually orthogonaldirections (x- and y-).

FIG. 9 illustrates global motion estimation between the image center ofa DRR and the image center of a corresponding x-ray image.

FIG. 10A schematically illustrates a mesh grid established for a DRR ofa target region, and a corresponding mesh grid established for an x-rayimage of the target region, in an embodiment in which the target islocated within the cervical region of the spine.

FIG. 10B schematically illustrates a mesh grid established for a DRR ofa target region, and a corresponding mesh grid established for an x-rayimage of the target region, in an embodiment in which the target islocated within the thoracic region of the spine.

FIG. 10C schematically illustrates a mesh grid established for a DRR ofa target region, and a corresponding mesh grid established for an x-rayimage of the target region, in an embodiment in which the target islocated within the lumbar region of the spine.

FIG. 11 illustrates a hierarchy of meshes for mesh nodal motionestimation, starting from a relatively course mesh and progressing ontofiner meshes.

FIG. 12 schematically illustrates the passing on of node estimation,from a course mesh resolution level onto a finer mesh resolution level.

FIG. 13 schematically illustrates the determination of a motion vectorfor a point of interest, by interpolation from surrounding nodes.

FIG. 14A schematically illustrates, in vectorial form, a full motionfield (reconstructed from many estimated local motion vectors), in anembodiment in which the target is located within the cervical region ofthe spine.

FIG. 14B schematically illustrates, in vectorial form, a full motionfield (reconstructed from many estimated local motion vectors), in anembodiment in which the target is located within the thoracic region ofthe spine.

FIG. 14C schematically illustrates, in vectorial form, a full motionfield (reconstructed from many estimated local motion vectors), in anembodiment in which the target is located within the lumbar region ofthe spine.

FIG. 15 is a schematic block diagram of a motion field generatorconfigured to generate a full motion field during non-rigid imageregistration of an object, in accordance with one embodiment.

FIG. 16 is a schematic block diagram of an apparatus for performingfiducial-less non-rigid image registration, in one embodiment.

FIG. 17A schematically illustrates target localization between a DRR ofthe target and an x-ray image of the target, in an embodiment in whichthe target is located within the cervical region of the spine.

FIG. 17B schematically illustrates target localization between a DRR ofthe target and an x-ray image of the target, in an embodiment in whichthe target is located in the thoracic region of the spine.

FIG. 17C schematically illustrates target localization between a DRR ofthe target and an x-ray image of the target, in an embodiment in whichthe target is located in the lumbar region of the spine.

FIG. 18 is a table of TRE (target registration error) values fordifferent targets located within the cervical, thoracic, and lumbarregions, in embodiments in which fiducials are used.

FIG. 19 is a table of TRE (target registration error) values fordifferent targets located within the cervical, thoracic, and lumbarregions, in embodiments in which fiducials are removed in CT data.

DETAILED DESCRIPTION

A method and system is presented for tracking and aligning a treatmenttarget, without using fiducial markers. An intensity-based, non-rigid2D-3D image registration method and system is performed. Anatomicalreference structures, for example skeletal or vertebral structures thatare rigid and easily visible in diagnostic x-ray images, are used asreference points, eliminating the need for fiducial markers which mustbe surgically implanted. The tracking method and system of the presentinvention is useful in image guided radiosurgery and radiotherapy, andis particularly useful for spinal applications, i.e. for trackingskeletal structures in the body, especially in the regions containing orlocated close to spinal vertebrae. The method and system of the presentinvention, however, can also be used in any other application in whichthere is a need to register one image onto a different image.

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be evident, however, toone skilled in the art that the present invention may be practicedwithout these specific details. In some instances, well-known structuresand devices are shown in block diagram form, rather than in detail, inorder to avoid obscuring the present invention. These embodiments aredescribed in sufficient detail to enable those skilled in the art topractice the invention, and it is to be understood that otherembodiments may be utilized and that logical, mechanical, electrical andother changes may be made without departing from the scope of thepresent invention.

Some portions of the detailed descriptions that follow are presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of acts leading to a desiredresult. The acts are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The present invention can be implemented by an apparatus for performingthe operations herein. This apparatus may be specially constructed forthe required purposes, or it may comprise a general purpose computer,selectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, and magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, or any type of media suitable forstoring electronic instructions, and each coupled to a computer systembus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method. For example, any of themethods according to the present invention can be implemented inhard-wired circuitry, by programming a general purpose processor or byany combination of hardware and software. One of skill in the art willimmediately appreciate that the invention can be practiced with computersystem configurations other than those described below, includinghand-held devices, multiprocessor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. The invention can also be practiced indistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a communications network. Therequired structure for a variety of these systems will appear from thedescription below.

The methods of the invention may be implemented using computer software.If written in a programming language conforming to a recognizedstandard, sequences of instructions designed to implement the methodscan be compiled for execution on a variety of hardware platforms and forinterface to a variety of operating systems. In addition, the presentinvention is not described with reference to any particular programminglanguage. It will be appreciated that a variety of programming languagesmay be used to implement the teachings of the invention as describedherein.

Furthermore, it is common in the art to speak of software, in one formor another (e.g., program, procedure, application . . . ), as taking anaction or causing a result. Such expressions are merely a shorthand wayof saying that execution of the software by a computer causes theprocessor of the computer to perform an action or produce a result.

For radiosurgical treatment of tumorous targets, the task offiducial-less tracking is to predict patient target movement betweenpre-operative patient CT scanning and patient radiation treatment, basedon the skeletal structures. Target movement is tracked by comparing thepre-operative 3D CT data and intra-operative x-ray 2D projection images,i.e. a 2D/3D image registration is performed. As well known, the basicproblem in image registration is to find the optimal transformation thatrelates different representations or images of the same object. A 2D/3Dregistration, in particular, seeks to determine a projection mapping ortransformation, from a 3D to a 2D coordinate system, such that points ineach space which correspond to the same anatomical point are mapped toeach other. In one embodiment, the transformation is represented, forexample, by a set of non-rigid transformation parameters (dx_(T),dy_(T), dz_(T), r, p, w), where (dx_(T), dy_(T), dz_(T)) represent thetranslations of the object, which may be a treatment target, and (r, p,w) represent global rigid rotations of the target.

In one embodiment, two orthogonal x-ray projections are used to solvefor these six parameters. In this embodiment, the registration in eachprojection is performed individually, and the results of theregistration for each projection are subsequently combined, to obtainthe six 3D transformation parameters. In other embodiments, however,different projections or combinations thereof may be used to solve forthe transformation parameters.

FIG. 1A provides an overall schematic block diagram of a fiducial-lesstracking method and system, in an embodiment of the invention in whichthe object being tracked is an anatomical region of a patient (e.g. aspinal region), and includes at least one treatment target and at leastone reference skeletal structure. In overview, FIG. 1A shows that 2D-3Dnon-rigid image registration is performed for each of a pair oforthogonal projections A and B. For each projection, digitallyreconstructed radiographs (DRRs) are first generated from the 3D CT scandata. As shown in FIG. 1A, the projection images A and B, acquiredintra-operatively in near real time, are registered onto theirrespective DRRs. To determine the change in patient position andorientation between the time of the pre-operative CT scan and the timeof radiosurgical treatment, local motion fields (dx_(A), dy_(A)) and(dx_(B), dy_(B)) are estimated in 2D, by using similarity measures tocompare the pixel intensities in the x-ray images and the DRR images.The 3D motion field is derived from the 2D local motion fields. A full3D motion field, derived from the local motion fields, includes 3Dtarget translations (dx_(T), dy_(T), dz_(T)) and global rigid rotations(r, p, w), which are a set of non-rigid transformation parameters thatrepresent the difference in the position and orientation of thetreatment target, as shown in the projection images A and B, as comparedto the position and orientation of the treatment target, as shown in theDRRs.

FIG. 1B illustrates the geometric relations between a three-dimensionaltreatment target, and two orthogonal 2D x-ray projections (labeled A andB in FIG. 1B), in an image registration method and system in accordancewith the present invention. A pair of cameras (or image receivers) A andB receive their x-ray projections from respective x-ray sources (notshown). In the coordinate system of the 3D scan, the x-axis is directedinward into the paper, and is not indicated in FIG. 1B. As explainedabove, the change in position of the treatment target is represented bythree translations and three global rigid rotations (dx, dy, dz, r, p,w).

In FIG. 1B, the orthogonal 2D projections A and B are viewed from thedirections o_(A)s_(A) and o_(B)s_(B), respectively. For each of theprojections A and B, FIG. 1B illustrates respective 2D planar coordinatesystems that are fixed with respect to the image plane thatcharacterizes each projection. The image planes A and B for theprojections A and B are thus defined by mutually orthogonal axes withinthe respective coordinate systems. These axes are shown in FIG. 1B as(x_(A),y_(A)) for projection A, and (x_(B), y_(B)) for projection B. Thedirection of the axis x_(A) in the 2D coordinate system for projectionA, and the direction of the x-axis in the 3D scan coordinate system, areopposite with respect to each other. The direction of axis x_(B) in thecoordinate system for projection B, and the direction of the axis x inthe 3D scan coordinate system, are the same.

For projection A, the 2D motion field (dx_(A), dy_(A)) is estimated byregistering the x-ray image that is projected onto the image plane A,with the corresponding reference DRR image. For projection B, the 2Dmotion field (dx_(B), dy_(B)) is estimated by registering the x-rayimage that is projected onto the image plane B, with the correspondingreference DRR image. Given the 2D motion fields (dx_(A), dy_(A)) forprojection A, and (dx_(B), dy_(B)) for projection B, the 3-D targettranslation (dx_(T),dy_(T),dz_(T)), as well as the global rigidrotations (r, p, w), can be obtained for both projections A and B, by astraightforward mathematical operation.

FIG. 2 illustrates a flowchart 100 of a non-rigid image registrationalgorithm. In the illustrated embodiment, the registration algorithm isused for a 2D/3D registration, performed for each projection describedin FIG. 1B. In particular, non-rigid image registration is performed inthe illustrated embodiment between a 2D DRR (reconstructed frompre-operative CT scan data) of a patient's target region, and anintra-operative (near real-time) x-ray projection image of the targetregion.

It is to be understood, however, that the method and system described inthis section can be used in any other type of image registration processbetween a first image acquisition of an object (which may be any kind ofobject and is not limited to a target region within a patient), and asecond image acquisition of the object. In one embodiment, the imagesfor which non-rigid image registration is performed are discretizedimages each characterized by an array of pixels, each pixel having anassociated pixel value representative of the intensity of the image at asurface unit area corresponding to the pixel.

As a first step, 2D DRRs (digitally reconstructed radiographs) may begenerated from pre-operative 3D scan data, in step 102. An improved DRRgeneration process can be implemented in step 102 to bring out theskeletal structures, which are usually not easily visible in the images,or even may be hidden. In step 102, the CT scan data are modified basedon a non-linear attenuation model that emphasizes the skeletalstructures and thus improves the quality of the DRRs. In the imageenhancement technique, implemented for the DRRs in step 103 in flowchart100, a top-hat filter is used to bring out the skeletal structures inthe DRRs generated in step 102.

In the illustrated embodiment, image registration is performed in aselected region of interest (ROI) within the enhanced DRR, in order toimprove efficiency. Accordingly, an ROI is defined in the DRR, in step104, after enhancement of the DRRs. An ROI selection process isperformed that is based on image entropy, is fully automatic, and doesnot require user interaction. Intra-operative 2D x-ray projection imagesare then generated, in near real time, in step 110. Image enhancement isperformed on the x-ray images, in step 115, using a top-hat filter as instep 103.

Non-rigid image registration is then performed between the enhancedx-ray images and the enhanced DRRs, within the ROI. In particular, asimilarity measure is used to compare the pixel intensities in the x-rayimages and the DRR images, in order to determine any change in theposition and/or orientation and/or physiological deformation of thepatient. In steps 120-150, a non-rigid deformation that describes realpatient movement and body deformation is defined. To define thenon-rigid deformation, a full motion field is constructed that iscomposed of many local motion fields, i.e. a plurality of locallyestimated motion vectors. To estimate local motion at a given point ofinterest within the ROI, a similarity measure based on pattern intensityis used to compare pixel intensities.

A full motion field that is composed of many local motions can describeany desired non-rigid deformation. Further, a full motion field derivedin this manner can account for non-rigid motions (translations and/orrotations) of the object, in addition to non-rigid deformations, betweendifferent image acquisitions of the object. In order to efficientlycompute the local motion vectors at any point of interest within theROI, hierarchical mesh motion estimation and multi-level block matching(performed in conjunction with an intensity-based similarity measure)are performed. These methods allow for a fast computation of the imageregistration algorithm 100. A smoothness constraint is imposed toreconstruct the motion field at mesh nodes in which mismatchingoccurred. The non-rigid transformation parameters for the non-rigidimage registration are then computed from the full motion field.

In the embodiment illustrated in FIG. 2, the non-rigid deformationsdescribed by the full motion field occur in between the acquisition ofthe 3D CT scan data of a treatment target region in a patient, and theacquisition of the x-ray projection images of the target region. In step120, a global translation of the entire image is first estimated. Theestimated global translation is used as the initial estimate for allfurther local motion estimation. In the next step 130, mesh nodal motionestimation is performed, using a hierarchical mesh structure designed toestimate the local motion in multiple levels. In the next step 140,motion field reconstruction is performed for those mesh nodes in which amismatch occurs. The reconstruction of the motion field is performed byimposing a smoothness constraint, which is based on the assumption thatlocal motions are continuous, because of matter coherence. In step 150,the local motion vector at any desired point of interest is derived byinterpolating from the nodal motions estimated for the mesh nodes thatsurround the point of interest. The full motion field is thenconstructed, using the local motion vectors derived for a plurality ofdesired points of interest.

In the final steps, shown as step 155 and step 160 in FIG. 2, thenon-rigid transformation parameters are derived from the full motionfield. In step 155, the target displacements are derived from the fullmotion field. In step 160, the average rigid transformation is derivedfrom the full motion field.

FIG. 3A schematically illustrates the generation of 2D DRRs from 3D scandata of a treatment target within an anatomical region of a patient, asperformed in step 102 in FIG. 2. In the illustrated embodiment, the 3Dscan data are CT scan data; alternatively, in other embodiments othertypes of 3D scan data that are known, e.g. MRI (magnetic resonanceimaging) scan data, PET (positron emission tomography) scan data, orultrasound scan data, may be used. In FIG. 3A, the volumetric 3D CTimage of the target is schematically referred to using reference numeral60. The DRRs 65A and 65B, shown in FIG. 3A, are artificial, synthesized2D images that represent the radiographic image of the target that wouldbe obtained if imaging beams, having the same intensity, position andangle as the beams used to generate the real time x-ray projectionimages, were transmitted through the target, and if the target werepositioned in accordance with the 3D CT scan data. The referencenumerals 50A and 50B illustrate the hypothetical positions and anglesfrom which the imaging beams would be directed through a targetpositioned in accordance with the CT volumetric image 60 of the target.

As known, CT scans can generate a 3D image of the target object, oneaxial slice at a time. Each axial CT slice can be viewed as beingcomposed of a plurality of individual volume elements, called CT voxels.Each CT voxel is thus disposed within one of a plurality of axial voxelslices, each voxel slice representing a corresponding axial slice of thetarget object. Each CT voxel is characterized by a numerical value,called the CT number, which represents the x-ray attenuationcharacteristics of the corresponding CT voxel. A CT image of a targetobject can be viewed as a map or distribution within the object of a 3Darray of CT numbers. The reconstruction of a CT image thus requires anaccurate measurement of the x-ray attenuations, and an accuratedetermination of CT numbers.

Typically, DRRs are generated by casting hypothetical beams or raysthrough the CT volumetric image of the target. Each ray goes through anumber of voxels of the 3D CT image 60. By integrating the CT numbersfor these voxels along each ray, and projecting onto an imaging plane(shown as 70A and 70B, respectively, in FIG. 3A), the resultant imagewould emulate the radiograph that would be obtained by passing rays fromhypothetical locations (50A and 50B, respectively) through a targetpositioned in accordance with the volumetric 3D image 60. The sum of CTnumbers is performed from the source point of the hypothetical ray, ontoa plane orthogonal to the central axis of the hypothetical beam. The sumis performed along each ray, an interpolated value being contributed byeach voxel through which the ray passes. Each voxel contribution isinterpolated over orthogonal segments along the beam path. Various knownray tracing algorithms may be used when generating DRRs.

Applications such as image-guided radiosurgery require that thecomparison between the DRRs and the real-time x-ray images, as well asthe subsequent adjustment of the position of the x-ray source, be madevery rapidly and accurately. In practice, the accuracy should be below 1mm, and the computation time should be on the order of a few seconds.Unfortunately, it is difficult to meet both requirements simultaneously.For example, the two different modality images, i.e. CT scan images andx-ray images, have different spatial resolution and image quality.Generally, x-ray image resolution and quality are superior to theresolution and quality of DRR images, which are only synthesized images.Typically, some structures in the DRR may appear more blurred(especially normal to the CT slice plane), compared to the x-ray image.Ideally, an optimal similarity measure for a 2D/3D registration processshould allow for an accurate registration to be achieved, despite suchdifferences. Also, DRR generation relies on a proper attenuation model.Because attenuation is proportional to the mass intensity of the targetvolume through which the beam passes, the exact relationship between thetraversed mass intensity and the CT image intensity needs to be known,in order to obtain an accurate modeling. Establishing this relationshipis difficult, however, so the linear attenuation model is often used, inconventional methods and systems for DRR generation.

As is known, the linear attenuation coefficient of a material isdependent on x-ray energy. CT machines and x-ray machines work atdifferent effective energies, however. As a result, the attenuationcoefficients measured by a CT scanner are different from the attenuationof a beam of x-rays passing through the target. The skeletal structuresin DRR images cannot be reconstructed very well using the linear model,the DRRs being only synthetic x-ray projection images. At the CT scanx-ray energies, the ratio of bone-to-soft-tissue attenuation is muchlower than at the x-ray radiographic projection energies. Thus, in a DRRproduced from a 3D CT volume, the image contrast from soft tissue tendsto be comparable with the image contrast from bone, reducing the clarityof bone details, for example.

The quality of DRR images relies on proper attenuation modeling, as wellas a proper interpolation scheme for interpolation the CT numbers. Inone embodiment, an improved DRR generation is accomplished during step102 (as shown in FIG. 2), by formulating an improved x-ray attenuationmodel for fiducial-less tracking, so that the DRRs become more like thereal x-ray projection images. A linear attenuation model is no longerassumed, and the CT numbers are modified in order to compensate for theabove-mentioned a difference in the bone-to-tissue attenuation ratio. Onthe basis of many experiments conducted with patient clinical data, thefollowing empirical equation was formulated to modify the original CTnumbers:C(x,y,z)=aC ₀(x,y,z)e ^(bC0(x,y,z))  (1)where C(x,y,z) represents the modified CT number of a 3D CT voxellocated at a point (x,y,z);a and b represent weighting coefficients;and C₀(x,y,z) represents the unmodified CT number, based on a linearattenuation model, of a 3D CT voxel having a location (x,y,z).

FIG. 3B provides a schematic plot of equation (1). In FIG. 3B, the curveidentified by reference numeral 270 represents CT numbers resulting fromthe non-linear attenuation model provided by the empirical formula (1),whereas the curve identified by reference numeral 280 represents CTnumbers resulting from a linear attenuation model. After modification ofthe CT numbers using equation (1), skeletal structures in the images areemphasized, and soft tissues are suppressed. In other words, use of theempirical formula (1) to process CT numbers during DRR generation servesto bring out the skeletal features against the tissue background, in theDRRs. In the region identified in FIG. 3B with reference numeral 290,the details of the rigid structures (e.g. skeletal structures) are moreeasily visible using the non-linear attenuation curve 290, since thedetails are spread over a broader range of intensity values, as comparedto the linear attenuation curve 280. Empirically, the weightingcoefficient b in equation (1) is 128 for 8-bit images, whereas theweighting coefficient a is slightly different for the cervical, thoracicand lumbar cases, respectively.

The interpolation scheme used in one embodiment to improve the qualityof DRRs is bi-linear interpolation. In this embodiment, bi-linearinterpolation is performed in step 210, to integrate the CT numbersalong the CT voxels that are encountered by each cast ray. In oneembodiment, the bi-linear interpolation is followed by a 1-D polynomialinterpolation over three voxel slices, for each voxel of interest. Thethree voxel slices include the voxel slice containing the voxel ofinterest, plus each adjacent voxel slice.

Fiducial-less tracking relies on skeletal reference structures that areusually not easily visible, or may even be hidden in the DRRs and in thex-ray projection images. Because fiducial-less tracking is based onregistration of such skeletal structures, both the DRR and the x-rayimages have to be enhanced to bring out the details of the vertebralstructures and improve their visibility. In one embodiment, therefore,image enhancement is undertaken for both the DRRs and the x-rayprojection images. In most thoracic and lumbar cases, the skeletalstructures are not easily visible or even hidden in DRR and X-rayimages. For these cases therefore, enhancement of the DRR and the x-rayimages is necessary, in order to make registration at all possible. Incervical cases, the skeletal structures of spine are well visible inboth the DRR and the x-ray images, but the details of the structures arestill not clear. Accordingly, in cervical cases, the DRR and the x-rayimages should be enhanced to improve the registration.

FIG. 4 illustrates exemplary images that have been enhanced to increasethe visibility of skeletal structures, using top hat filtering. In theembodiment illustrated in FIG. 4, a top-hat filter was designed and usedto enhance the x-ray images (step 115 in FIG. 2) and to enhance the DRRimages (step 103 in FIG. 2). In particular, the skeletal structures inthe images have been enhanced, i.e., brought out, by applying a top hatfilter operator to the pixels of the x-ray projection images and the DRRimages. As known, a top hat filter is a nonlinear operator that findsthe brightest pixels in two different size neighborhoods, then keeps theextreme values. In one embodiment, the top hat filter operates asfollows: if the brightest value in the smaller neighborhood region isgreater that the value in the larger neighborhood, by an amountdetermined by a user-entered threshold, then the pixel remains,otherwise it is eliminated. As a result of applying a top hat filter tothe images, it is possible to locate features of interest.

In one embodiment, the top-hat filter is designed by using a weightedcombination of image opening and closing with a certain structuralelement. The top hat filter operator is defined mathematically asfollows:

$\begin{matrix}\begin{matrix}{f_{e} = {f + {w \times \left\lbrack {f - {\gamma_{B}(f)}} \right\rbrack} - {b \times \left\lbrack {{\varphi_{B}(f)} - f} \right\rbrack}}} \\{= {f + {w \times W\; T\;{H(f)}} - {b \times B\; T\;{H(f)}}}}\end{matrix} & (2)\end{matrix}$where f_(e) represents the enhanced image, resulting from theapplication of the top hat filter operator to each pixel in the originalimage;f represents the original image;w and b represent weighting coefficients,γ_(B)(f) represents a structural element for the opening of the originalimage f, andφ_(B)(f) represents a structural element for the closing of the originalimage f.

In expression (2) above, WTH(f)=f−γ_(B)(f) is called a white top-hatfilter, whereas BTH(f)=φ_(B)(f)−f is called a black top-hat filter. Thestructural elements γ_(B)(f) and φ_(B)(f) are masks that are used toperform the basic morphological operation. The sizes of the structuralelements vary slightly for cervical, thoracic, and lumbar applications.The empirical values are determined experimentally. The weightingcoefficients w and b are determined adaptively by the amplitudes ofWTH(f) and BTH(f), respectively. Empirically, the values of theweighting coefficients w and b have been found to be about 1 each (w=1,b=1), for a cervical case in which less tissue is present. In the lumbarcase, in which more tissue is present, the values of w and b have beenfound to be greater than about 2 each (w>2, b>2). In the lumbar case,the weighting process brings out the skeletal structures to a greaterdegree, compared with the cervical case.

In one embodiment, image registration is conducted only in a certainregion of interest (ROI) defined in the DRR. The ROI contains thetreatment target (e.g. a tumor or lesion). In one embodiment, imageentropy is specifically defined, in step 104 in FIG. 2. In this way, theROI can be automatically selected, for optimum registration, minimizingor even eliminating user interaction. Because image registration relieson the image content or image information, in this embodiment the ROI isoptimized to contain as much information as possible.

The Shannon entropy, known from conventional communication theory, iscommonly used as a measure of information in signal and imageprocessing. It is defined as H=−Σ_(i) ^(n)p_(i) log p_(i), where Hrepresents the average information supplied by a set of n symbols whoseprobabilities are given by p₁, p₂, . . . , p_(n). When applied to thepixels of each image (as enhanced in steps 103 or 115 in FIG. 2), theShannon entropy for each image is defined by: H=−Σ_(I)p(I) log p(I),where I is the image intensity level, and p(I) is the probability of animage intensity value I occurring within the ROI. In the originalformulation by Shannon, any change in the data that tends to equalizethe probabilities p₁, p₂, . . . , p_(n) increases the entropy, asobserved by Shannon. For a given image, the Shannon entropy isconventionally calculated from a image intensity histogram, in which theprobabilities p₁, p₂, . . . , p_(n) are histogram entries.

In one embodiment, the Shannon entropy H is modified, based on the factthat the skeletal structures occur in bright areas. In this embodiment,a modified Shannon entropy is used for each image, which is defined asfollows:H=−Σ _(I) Ip(I)log p(I),  (3)where again I is the image intensity level, and p(I) is the probabilityof the image intensity value I occurring within the ROI. In step 104(shown in FIG. 2), the modified Shannon entropy is first determined forthe enhanced DRR image. Once the modified Shannon entropy H iscalculated, an ROI is then automatically selected by determining theregion within the DRR for which the entropy H is maximized. Subsequentsteps in the image registration process (steps 120-150 in FIG. 2) takeplace only within the ROI.

Restricting the image registration process to within a ROI has severaladvantages. One advantage is that such a restriction can speed up theregistration process, since the registration needs to be performed onlyfor the ROI. For example, the similarity measure needs only be computedfor the ROI, and block matching need only be performed within the ROI.Further, the registration process is more accurate when limited to anarea within the ROI. The more limited the region in which registrationis conducted, the less likely it is that structures within the ROI wouldhave moved relative to each other between the time of the pre-operativeCT scans and the time of the medical treatment.

Based on the improved and enhanced DRRs (step 103 in FIG. 2), and theenhanced x-ray projection images (step 115 in FIG. 2), in which theskeletal reference structures have been brought out to makefiducial-less tracking possible, a non-rigid deformation of theanatomical region is determined in steps 120-150 (shown in FIG. 2). Inthis patent, a ‘rigid body’ assumption, i.e. which is often made inimage registration applications, and which assumes that between imageacquisitions, the anatomical and pathological structures of interest donot deform or distort, is not made. There is no longer a need topreserve the ‘rigid body’ constraints, i.e. to require that the body berigid and not undergo any local variations during the transformation.Based on an abundance of observations and analyses on clinical patientdata, in the present patent a non-rigid deformation is assumed, in lieuof a rigid transformation, to obtain an improved description of the realpatient movement and body deformation. By computing a non-rigiddeformation field, patient position/orientation can be more reliablymonitored corrected during the initial alignment, as well as throughoutthe entire treatment.

A non-rigid image registration allows the inherent local anatomicalvariations that exist between different image acquisitions to beaccounted for, in contrast to a rigid image registration which does notallow the overcoming of such variations. Non-rigid registration definesa deformation field that provides a translation or mapping for everypixel in the image. In one embodiment, a full motion field, composed ofmany local motion vectors or fields, is computed in order to derive thenon-rigid deformation field.

In order to estimate local motion fields, in one embodiment, amulti-level block matching method is used in conjunction with asimilarity measure based on pattern intensity. This approach allows thelocal motion to be rapidly and accurately estimated in most parts of theROI. Multi-level block matching, which allows for computationalefficiency, is described in conjunction with a rigid registrationalgorithm, in a commonly owned application, U.S. Ser. No. 10/652,786(the “'786 application”), incorporated by reference in its entirety. Asimilarity measure based on pattern intensity, used in conjunction witha registration algorithm based on rigid transformations, i.e. the “FAST6D algorithm” developed by Accuray, Inc. for use with the Cyberkniferadiosurgery system, is described in full in commonly ownedapplications, U.S. Ser. Nos. 10/652,786 (the “'786 application”),10/652,717 (the “'717 application”), and 10/652,785 (the “'785application”), which are all incorporated by reference in theirentireties. In the present patent, the pattern intensity basedsimilarity measure and the multi-level block matching method are used inconjunction with a registration algorithm based on a non-rigid (ratherthan a rigid) transformation. The pattern intensity-based similaritymeasure, originally developed for a rigid image registration algorithm,provides a powerful and efficient technique for solving the 2D/3D imageregistration problem, also in a non-rigid framework.

In one embodiment, block matching is performed, i.e. a small blockcentered around a point of interest is used in order to locally estimatethe displacements at each desired point within the ROI. As known, whenusing block matching to register a first image onto a second image, thefirst image is divided into different blocks, typically rectangularboxes of equal size. Each point of interest, which may be a mesh node,or may be a non-node pixel that is surrounded by mesh nodes, is taken asthe center of one of the blocks. These blocks are then translated so asto maximize a local similarity criterion, which in one embodiment is thepattern intensity based similarity measure, described above.

In block matching methods, it is generally assumed that each pixel in ablock has the same motion, and a block matching algorithm is typicallyused to estimate the motion vectors for each block. In a block matchingalgorithm used in one embodiment, a search is conducted for a matchingblock in the second image, in a manner so as to maximize a measure ofsimilarity, based on pattern intensity, between the respective blocks.The search is for a location of the maximum in the similarity measurefunction, the maximum representing the existence of a matching block inthe second image. The search may be conducted within a search windowthat is defined around the point of interest and that contains theblock.

In any block matching algorithm, it is important to optimize the searchstrategy, and to select an appropriate block size. For small blocks, thetranslational rigid model is typically assumed. Even though rigidrotations or some other complicated deformations exist, the rigid bodytranslation model is valid for estimating the translations for the blockcenter point. When rotations or other deformations exist in addition tothe translations, the accuracy increases with decreasing block size, anddecreases with increasing block size. With the use of smaller blocksizes, however, the possibility of mismatching increases. In oneembodiment, a block size selection strategy is adopted in which it isassumed that larger blocks are needed for larger displacements, and thatsmaller blocks are need for smaller displacements.

FIGS. 5A and 5B schematically illustrate local motion estimation for apoint of interest within a target in a patient, using block matching. Inthe embodiment illustrated in FIG. 5A, the target is located in thecervical region of the spine, whereas in the embodiment illustrated inFIG. 5B, the target is located in the thoracic region. In both FIGS. 5Aand 5B, the left and the right pictures are the DRR and X-ray images,respectively. In each figure, a small block 203A is defined around apoint of interest 205 in the DRR. Also, a search window 207 thatencompasses the block 203 is defined in the DRR. The matching block inthe x-ray image is indicated in the figures with reference numeral 203B.In the embodiment illustrated in FIGS. 5A and 5B, the size of the searchwindow 207 is 48 mm×48 mm, and the block size is 15×15 mm. It can beseen, simply by visual inspection, that the point of interest 205 iswell located in the X-ray image.

FIG. 6 schematically illustrates a multi-resolution imagerepresentation, when implementing multi-level block matching, usingmultiple candidates. Multi-level block matching is a fast search methodthat uses the displacement estimates made at a lower level as theinitial results for subsequent search phases. The basic idea inmulti-level block matching is to match the images at each of a pluralityof resolution levels, successively, starting from the lowest resolutionlevel and moving up to the highest resolution level. The full-sizeimage, having the highest resolution level, is shown at the bottom inFIG. 6, as level 1. The upper images (level 2 and level 3) havesuccessively lower spatial resolutions, the image having the lowestresolution being shown as level 3. The lower resolution images areobtained by lower pass filtering, and sub-sampling the full-size images.

In FIG. 6, assuming that the full image block size is W×H in Level 1,the block sizes are

$\frac{W}{2} \times \frac{H}{2}\mspace{14mu}{and}\mspace{14mu}\frac{W}{4} \times \frac{H}{4}$in Level 2 and Level 3, respectively, as indicated in the figure. In thelowest resolution level (Level 3), a large search range is used toenable estimation of large displacements. A very small search range (−2,+2) is used in the rest of the resolution levels.

The results at the lower resolution level serve to determine roughestimates of the displacements. The output at the lower level is thenpassed onto the subsequent higher resolution level. The estimated motionvector (in most cases, a translation vector) for the block issuccessively refined, using the higher resolution images. In the finalmatching results, the accuracy of the estimated translations depends onthe spatial resolution of the highest resolution images (shown as level1 in FIG. 6).

There is some risk in multi-level matching. It is possible that theestimate at lower levels may fall in a local maximum, and far away fromthe global maximum that is being sought. In this case, further matchingsat subsequent higher resolution levels may not converge to its globalmaximum. To overcome this risk, multiple candidates are used for theestimates, in one embodiment. Many candidates that have shown optimalmatching results are passed on from the lower levels to the higherresolution levels. The more candidates that are used, the more reliableare the estimates. In one embodiment, the best candidates are ranked bythe similarity measure function values.

In one embodiment, a similarity measure based on pattern intensity isused, in conjunction with multi-level block matching. As mentionedearlier, this similarity measure is a key element contributing to thesuccess of the “FAST 6D algorithm,” described in the commonly owned '786application, '717 application, and '785 application. In one embodiment,the similarity measure is determined by forming a difference imagebetween the “live” (or near real time) x-ray projection images and theDRR images, and applying upon each pixel of the difference image apattern intensity function. Specifically, the difference imageI_(diff)(i,j) is formed by subtracting a corresponding pixel value ofthe pre-operative DRR image from each pixel value of the intra-operativex-ray projection image, within the ROI:I _(diff)(i,j)=I _(Live)(i,j)−I _(DRR)(i,j)  (4)

In equation (4), I(i,j) represents the image intensity value of a pixellocated at the i-th row and j-th column of each pixel array for therespective image. Specifically, I_(diff)(i, j) represents an array ofpixel values for a difference image formed by subtracting thecorresponding pixel values of the second image from each pixel value ofthe first image. I_(live)(i,j) represents the (i,j)-th pixel value ofthe first image of the object. I_(DRR)(i,j) represents the (i,j)-thpixel value of the second image of the object. The similarity measureoperates on this difference image, and is expressed as the summation ofasymptotic functions of the gradients of the difference image over thepixels within a neighborhood R:

$\begin{matrix}{\sum\limits_{i,j}{\sum\limits_{k,{l \Subset R}}\frac{\sigma^{2}}{\sigma^{2} + \left( {{I_{diff}\left( {i,j} \right)} - {I_{diff}\left( {{i + k},{j + l}} \right)}} \right)^{2}}}} & (5)\end{matrix}$

In equation (5) above, the constant σ is a weighting coefficient for thepattern intensity function. The sensitivity of the solution to thevariation of x-ray image can be minimized by careful selection of thisconstant. The larger the weighting coefficient, the more stable theresults. However, the choice of a entails a tradeoff between stabilityand accuracy. When the value of σ is too large, some small details inthe images cannot be reflected in the similarity measure. Based on theexperiments, the empirical value for σ is in the range from about 4 toabout 16, in one embodiment.

FIG. 7 schematically illustrates a neighborhood R for calculating asimilarity measure based on pattern intensity. As seen from FIG. 7, theneighborhood R in the illustrated embodiment is defined so that thegradients of the difference image can be considered in at least fourdirections (horizontal, vertical, 45° diagonal and −45° diagonal). Whenthe neighborhood R is defined in this manner, equation (5) for thesimilarity measure becomes:

$\begin{matrix}{{\sum\limits_{i,j}\frac{\sigma^{2}}{\sigma^{2} + \left( \left( {{I_{diff}\left( {i,j} \right)} - {I_{diff}\left( {i,{j - 1}} \right)}} \right)^{2} \right.}} + {\sum\limits_{i,j}\frac{\sigma^{2}}{\sigma^{2} + \left( \left( {{I_{diff}\left( {i,j} \right)} - {I_{diff}\left( {{i - 1},j} \right)}} \right)^{2} \right.}} + {\sum\limits_{i,j}\frac{\sigma^{2}}{\sigma^{2} + \left( \left( {{I_{diff}\left( {i,j} \right)} - {I_{diff}\left( {{i - 1},{j - 1}} \right)}} \right)^{2} \right.}} + {\sum\limits_{i,j}{\frac{\sigma^{2}}{\sigma^{2} + \left( \left( {{I_{diff}\left( {i,j} \right)} - {I_{diff}\left( {{i - 1},{j + 1}} \right)}} \right)^{2} \right.}.}}} & (6)\end{matrix}$

Equations (5) and (6) for pattern intensity have several advantages.First, the difference image filters out the low frequency part thatpredominantly consists of the soft tissues, and keeps the high frequencypart that predominantly consists of the skeletal structures. Thisfeature makes the algorithm robust to some brightness intensitydifference between live and DRR images. Second, because of theasymptotic function, the measure is less affected by the pixels whoseintensity value slightly deviates from its neighboring pixels. Thesetypes of pixels are thought to contain random noise. Third, because theasymptotic function quickly approaches to zero when the variableincreases, large intensity differences such as image artifacts have thesame effects on the similarity measure regardless of their magnitude.Due to this feature, the pattern intensity is less sensitive to imageartifacts.

FIGS. 8A-8B provides a plot of the similarity measure function that wasused for the local motion estimation illustrated in FIGS. 5A-5B, andthat is based on equations (5) and (6). The similarity measure functionis plotted with respect to translations in two mutually orthogonaldirections (x- and y-). The existence of the global maximum is clearlyshown, in both FIG. 8A and FIG. 8B. Several local maximum points alsoexist in FIGS. 8A and 8B, however. This indicates that the use ofmultiple candidates may be necessary in multi-level block matching, asexplained above.

The estimation of local motion fields using block matching together withhierarchical mesh motion estimation, as well as the reconstruction ofthe full motion field from the plurality of locally estimated motionfields, are performed in steps 120-150 of the flowchart shown in FIG. 2.Fast generation of the full motion field is achieved by usinghierarchical mesh tracking, and using SIMD (single instruction multipledata) technology to perform image computation in parallel.

In one embodiment, a global translation of the entire image (measured asa translation of the image center of the image) is first estimated, thenused as the initial estimates for all further local motion estimation.In other words, a rough estimate is made of the center displacement forthe entire image, and is used as the starting estimate for all localdisplacements. Referring back to FIG. 2, the first step (indicated withreference numeral 120 in FIG. 2) in generating a full motion field for atarget, between the pre-operative scan and the intra-operativetreatment, is the step of estimating a global translation for the entireimage, or equivalently, estimating the center displacement of the image.

FIG. 9 illustrates the estimation of global motion (in this case,translation only), between the image center of a DRR and the imagecenter of a corresponding x-ray image. In the illustrated embodiment,the image center is used as the block center. The step of globaltranslation estimation is very important, because any failure duringthis step will affect the rest of the local motion estimation process.To prevent any possibility of mismatching, a very large image block isused in the illustrated embodiment. The maximum tracking range can becalculated as the difference between the block size and the entire imagesize. For example, if the matching size is 80×80 mm, the maximum trackedtranslation is 60 mm. In the embodiment illustrated in FIG. 9, a blockhaving a size of 160×160 pixels (64 mm×64 mm) is used. The search windowin the illustrated embodiment is the entire image. The maximum trackrange for the illustrated embodiment is (−50 mm, +50 mm).

After global motion estimation, the next step 130 (see FIG. 2) is meshmotion estimation. In this step, a hierarchical 2D mesh structure isdesigned in order to estimate local motion in multiple levels. As known,a 2D mesh (or a 2D mesh grid) refers to a tesselation of a 2D regioninto polygonal patches or elements, whose vertices are called meshnodes. Unlike block matching algorithms, which generally assume onlytranslational motion, 2D mesh models allow for spatial transformationsto model rotations, scalings, and deformations of the object that wasimaged, in addition to translations of the object. Compared to blockmatching algorithms, therefore, mesh-based methods may produce a moreaccurate representation of the motion field, for example may generatecontinuously varying motion fields.

FIG. 10A schematically illustrates a mesh grid 200 established for a DRRof a target region, and a corresponding mesh grid 202 established for anx-ray image of the target region, in an embodiment in which the targetis located within the cervical region of the spine. FIG. 10Bschematically illustrates a mesh grid 204 established for a DRR of atarget region, and a corresponding mesh grid 206 established for anx-ray image of the target region, in an embodiment in which the targetis located within the thoracic region of the spine. FIG. 10Cschematically illustrates a mesh grid 208 established for a DRR of atarget region, and a corresponding mesh grid 210 established for anx-ray image of the target region, in an embodiment in which the targetis located within the lumbar region of the spine.

With a 2D mesh, motion compensation within each mesh element or patchmay be accomplished by deriving a spatial transformation between theimages, where the transformation parameters are computed from the nodalmotion vectors, i.e. from the motion vectors that are estimated for themesh nodes that are located at the vertices of the mesh. In other words,mesh-based motion estimation consists of finding a spatialtransformation that best maps one set of mesh elements in a first imageacquisition onto another set of mesh elements in a second imageacquisition. In particular, mesh motion estimation consists of findingthe vertices of corresponding mesh elements in the other image, i.e.finding the corresponding mesh nodes in the other image, such thaterrors are minimized in the overall motion field. Typically, a number ofmesh nodes are selected in one image, and the corresponding mesh nodesin the other image are estimated.

For any pixel located within a mesh element (as opposed to being locatedon the vertices of the mesh elements), the mapping between differentimage acquisitions is performed through interpolation. The local motionvectors for such pixels are estimated by interpolating from the nodalmotion vectors that were estimated for the mesh nodes that surround thepixel.

In one embodiment, hierarchical mesh motion estimation may be performed.By hierarchical mesh motion estimation, it is meant that nodal motion isestimated for the mesh nodes that define the mesh structure, for each ofa plurality of mesh resolution levels. Motion estimation performed witha course mesh provides the initialization for the subsequent (finer)resolution levels of the mesh. To estimate the motion of each mesh node,multi-level block matching may be performed.

FIG. 11 illustrates a mesh hierarchy, during mesh motion estimation. Asseen from FIG. 11, the mesh hierarchy starts from a relatively coursemesh, 220, and progresses onto finer meshes, illustrated as 222 and 224.Using the global translations (estimated in step 120 as the initialestimates), nodal motion for the mesh nodes located at the vertices ofthe most course mesh is first calculated. These estimates are thenpassed onto the subsequent, finer mesh. At each level, nodal motion isupdated, using a smaller search range. Finally, the motion vectors forthe mesh nodes at the final one of the mesh resolution levels(characterized by the finest mesh resolution level) are refined. For allthe nodes, multi-level block matching with multiple candidates is used,together with the pattern-intensity based similarity measure, given inequations (5) and (6).

FIG. 12 schematically illustrates the passing on of node estimation,from a course mesh resolution level onto a finer mesh resolution level.At each mesh resolution level after the first level, the mesh nodesinclude both 1) mesh nodes generated at a previous mesh resolutionlevel; and 2) mesh nodes that are newly added at the current meshresolution level. In the illustrated embodiment, the initial estimatesfor nodal motion vectors, for the newly added nodes at the current mesh,are obtained by linear interpolation of the existing nodal motionvectors, at the previous mesh resolution level. During this process, anyunreliable mesh node needs to be detected, so that only reliable nodesare passed onto the subsequent mesh level.

FIG. 12 illustrates how such a detection can be performed, using a meshnode referred to in FIG. 12 as ‘node 5.’ In the illustrated embodiment,the difference between the motion vector (in this case, translationvector) of node 5, and the median motions (translations) computed fromits 9 surrounding nodes (nodes 1-4, 6-9 in FIG. 12) is taken. As seenfrom FIG. 12, the translation of node 2 is the average of thetranslations of node 1 and node 3; the translation of node 4 is theaverage of the translations of node 1 and node 7; the translation ofnode 6 is the average of the translations of node 3 and node 9; and thetranslation of node 8 is the average of the translations of node 7 andnode 9. The translation of node 5 is the average of the translations ofnodes 1, 3, 7, and 9. If the difference between the translation of node5 and the median translations computed from its 9 neighboring nodes isless than a predefined threshold, the node 5 is considered as a reliablenode. Otherwise, it is considered as an unreliable node, and itstranslations are replaced with the median values and passed to thesubsequent mesh.

For most mesh nodes, the estimates of motion are reliable and accurate.For a few nodes where mismatching may occur and the estimation may notbe reliable, the displacements need to be reconstructed by thesurrounding node displacements. Accordingly, the next step in theregistration algorithm flow chart in FIG. 2 is step 140 of motion fieldreconstruction, during which the motion field is reconstructed fromsurrounding nodes, for those nodes in which mismatching occurs. Theinaccurate nodal motion vectors can be detected by using 3×3 medianfiltering.

Local motion estimation relies on the local image content. In somesmooth local regions, mismatching may occur. During mesh motionestimation, the estimation in most nodes is pretty accurate. For a fewnodes where mismatching occurs, the motions should be reconstructed fromtheir surrounding nodes. What is known a priori is matter coherence ofbone and tissue, and accordingly, the smoothness of local motion. Inother words, the estimated local motion vectors are thought to be smoothand continuous, because of matter coherence. By imposing thisphysically-based smoothness constraint, a cost function is formulated toreconstruct the motion field.

In one embodiment, the cost function is expressed mathematically asfollows:E(d)=∫∫β(d−u)² dxdy+λ∫∫(d _(,x) ² +d _(,y) ²)dxdy  (7)In equation (7) above, E(d) represents the cost function, d represents adesired local estimate for a nodal motion vector at coordinates (x,y), urepresents a locally estimated nodal motion vector at coordinates (x,y),and β represents a reliability constant that ranges from 0 to 1, whereβ=0 indicates an unreliable estimation, and β=1 indicates a reliableestimation.

By performing a finite difference of the derivatives over the meshgrids, a discretized form for the cost function in equation (7) isexpressed as:E(d _(i,j))=ΣΣβ_(i,j)(d _(i,j) −u _(i,j))²+λΣΣ[(d _(i,j) −d_(i−1,j))²+(d _(i,j) −d _(i,j−1))²]  (8)where u_(i,j) represents the locally estimated translations, d_(i,j) isthe local motion desired, β_(i,j)=1 if the estimation is reliable andβ_(i,j)=0 if the estimation is unreliable.The first term on the right side of equation (8) reflects the fidelityto the observed data in the reconstruction. The second term imposes thesmoothness constraints on the motion field in two spatial directions.

The minimization of the cost function given by equation (8) results in asystem of simultaneous linear equations

$\begin{matrix}\begin{matrix}{\frac{\delta\;{E\left( d_{i,j} \right)}}{\partial d_{i,j}} = {{\left( {\beta_{i,j} + {4\lambda}} \right)d_{i,j}} - {\lambda\left( {d_{{i - 1},j} + d_{{i + 1},j} + d_{i,{j - 1}} + d_{i,{j + 1}}} \right)} -}} \\{\beta_{i,j}u_{i,j}} \\{= 0}\end{matrix} & (9)\end{matrix}$In one embodiment, the iterative algorithm of successive-over relaxation(SOR), which is fast and convergent, is used to solve the equations:d _(i,j) ^((n+1)) =d _(i,j) ^((n))−ω[(β_(i,j)+4λ)d _(i−1,j) ^((n))−λ(d_(i−1,j) ^((n)) +d _(i+1,j) ^((n))+_(i,j−1) ^((n)) +d _(i,j+1)^((n)))−β_(i,j) u _(i,j)]/(β_(i,j)+4λ)  (10)

Once all the nodal motion vectors have been estimated at all the meshnodes, the translations for any point (or pixel) inside the ROI can becomputed by interpolation. FIG. 13 schematically illustrates thedetermination of a motion vector for a point of interest, byinterpolation from surrounding nodes. In the illustrated embodiment,quadratic interpolation is performed, using the 9 nearest nodes, and 9shape functions are used.

Assuming the motion vector (dx(i), dy(i)) for nine nodes, the motionvector (dx, dy) at the point of interest is computed using the followingexpressions:

$\begin{matrix}{{{dx} = {\sum\limits_{i = 1}^{9}{{N(i)}{{dx}(i)}}}},{{dy} = {\sum\limits_{i = 1}^{9}{{N(i)}{{dy}(i)}}}},} & (11)\end{matrix}$where N(i) is the shape function for the node (i), and where N(i) forI=1, 2, . . . 9 are given as follows:N(1)=(1−ξ)(1−η)/4−(N ₈ +N ₅)/2,N(2)=(1−ξ)(1−η)/4−(N ₅ +N ₆)/2,N(3)=(1+ξ)(1+η)/4−(N ₆ +N ₇)/2,N(4)=(1−ξ)(1+η)/4−(N ₇ +N ₈)/2,N(5)=(1−ξ²)(1−η)/2,N(6)=(1−ξ)(1−η²)/2,N(7)=(1−ξ²)(1+η)/2,N(8)=(1−ξ)(1−η²)/2,N(9)=(1−ξ²)(1−η²).

Using steps 120, 130, and 140, described above, the local motion vectorscan be estimated for a plurality of points of interest within the ROI.The full motion field is obtained as a composite or superposition of allof the local motion vectors that are estimated for the many points ofinterest that have been selected for motion estimation.

FIG. 14A schematically illustrates, in vectorial form, a full motionfield (reconstructed from many estimated local motion vectors), in anembodiment in which the target is located within the cervical region ofthe spine. FIG. 14B schematically illustrates, in vectorial form, a fullmotion field (reconstructed from many estimated local motion vectors),in an embodiment in which the target is located within the thoracicregion of the spine. FIG. 14C schematically illustrates, in vectorialform, a full motion field (reconstructed from many estimated localmotion vectors), in an embodiment in which the target is located withinthe lumbar region of the spine.

The final step in the image registration process is target localization,namely deriving the target translations and rotations from the fullmotion field that has been determined. In one embodiment, non-rigidimage registration seeks to determine a projection mapping ortransformation between different coordinate systems in respective imageacquisitions such that points in each space which correspond to the sameanatomical point are mapped to each other. In one embodiment, thetransformation is represented by a set of non-rigid transformationparameters (dx_(T), dy_(T), dz_(T), r, p, w), where (dx_(T), dy_(T),dz_(T)) represent the translations of the target, and (r, p, w)represent rotations of the target.

Referring back to FIG. 2, the 3-D target translation(dx_(T),dy_(T),dz_(T)) can easily be obtained in step 155 (shown in FIG.1), given the 2D local motion fields (dx_(A), dy_(A)) for projection A,and (dx_(B), dy_(B)) for projection B, using the following expressions:

$\begin{matrix}\begin{matrix}{{{dx}_{T} = {\left( {{dx}_{TA} + {dx}_{TB}} \right)/2}},} \\{{{dy}_{T} = {\left( {{dy}_{TA} - {dy}_{TB}} \right)/\sqrt{2}}},} \\{{dz}_{T} = {\left( {{dy}_{TA} + {dy}_{TB}} \right)/\sqrt{2}}}\end{matrix} & (13)\end{matrix}$

The global rigid rotations (r, p, w) can be calculated from the motionfields (dx_(A), dy_(A)) in projection A and (dx_(B), dy_(B)) inprojection B. Using the target as the rotation center, global rigidrotations are useful for position and rotation correction andcompensation during initial patient alignment and treatment. Because thetarget translation is already calculated, the calculation of the globaltranslation is not needed. To get the three rotations in 3D patientcoordinates, three 2D in-plane rotations are first computed, includingthe in-plane rotations θ_(A) and θ_(B) in projections A and B,respectively, and the in-plane rotation θ_(x) in a plane perpendicularto the inferior-superior axis. Approximately, the global rotations canbe expressed as:

$\begin{matrix}\begin{matrix}{{r = \theta_{x}},} \\{{p = {\left( {\theta_{B} - \theta_{A}} \right)/\sqrt{2}}},} \\{{w = {\left( {\theta_{B} + \theta_{A}} \right)/\sqrt{2}}},}\end{matrix} & (14)\end{matrix}$

Estimation of θ_(A) and θ_(B) is directly based the 2D motion fields inprojections A and B, respectively. To estimate θ_(x), a plane is firstdefined, which passes the target point and is perpendicular to the axisx in the 3D patient coordinate system. Then the motion field iscalculated from the two motion fields (x_(A), y_(A)) and (x_(B), y_(B))in projections A and B, respectively.

Assuming (dx, dy) is the motion field in the corresponding coordinate(x, y) and θ is the global rotation. When the rotation is small (<10°),the following transformation equation is valid:

$\begin{matrix}{\begin{Bmatrix}{dx} \\{dy}\end{Bmatrix} = {\begin{bmatrix}0 & {- \theta} \\\theta & 0\end{bmatrix}\begin{Bmatrix}x \\y\end{Bmatrix}}} & (15)\end{matrix}$

Given (dx,dy) and (x,y) in many points, θ can be easily calculated usingleast square minimization method

$\begin{matrix}{\theta = \frac{\sum\limits_{i}\left( {{{x(i)}{\mathbb{d}{y(i)}}} - {{y(i)}{\mathbb{d}{x(i)}}}} \right)}{\sum\limits_{i}\left( {{{x(i)}{x(i)}} + {{y(i)}{y(i)}}} \right)}} & (16)\end{matrix}$Using equations (14) and (16) above, the average rigid transformationparameters can be obtained, in step 160 illustrated in FIG. 2.

FIG. 15 is a schematic block diagram of an apparatus 300 for generatinga motion field during non-rigid image registration of an object, inaccordance with one embodiment. The apparatus 300 can generate, forexample, a full motion field of a target region of a patient's anatomy(for example, the spine), between the acquisition of pre-operative 3Dscan data of the target region and the acquisition of intra-operativex-ray projection images of the target region. The full motion field,composed of many local motion vectors, can take into account non-rigiddeformations of the target region, as well as non-rigid translations androtations. In this embodiment, the 2D/3D registration problem is one offinding the non-rigid transformation parameters that best align thecoordinate system of the DRRs (generated from the pre-operative 3D scandata) with that of the intra-operative x-ray projection images.

In one embodiment, the apparatus 300 is configured to perform meshmotion estimation, and to perform multi-level block matching at eachmesh node. In this embodiment, the apparatus 300 includes: 1) a meshgrid generator 310 configured to generate in the DRRs a mesh griddefined by a plurality of mesh nodes or vertices; 2) a nodal motionestimator 320 configured to estimate at least one nodal motion vectorfor each mesh node; and 3) a motion field interpolator 330 configured todetermine a local motion vector for each of a plurality of points ofinterest within the DRR, by interpolating from the surrounding meshnodes, i.e. by interpolating from the nodal motion vectors that havebeen estimated for the mesh nodes that surround each of the plurality ofpoints of interest. In an embodiment in which the DRR has been croppedso as to include a specific ROI, so that image registration isrestricted to an ROI within the DRR, the motion field interpolator 330determines the local motion vectors for each of a plurality of points ofinterest within an ROI defined within the DRR. The ROI can be defined soas to maximize image entropy within the ROI, as explained before.

In one embodiment, the system 300 performs hierarchical mesh tracking,i.e. mesh nodal motion estimation is performed for the mesh nodes of themesh structures defined at each of a plurality of mesh resolutionlevels. Preferably, these mesh resolution levels are successivelyincreasing mesh resolution levels, i.e. the mesh grid at each successivemesh resolution level has a number of mesh nodes that is greater,compared to the number of mesh nodes at each previous mesh resolutionlevel. In this embodiment, the mesh grid generator 310 is configured torepeat, at each of a plurality of mesh resolution levels, the act ofgenerating a mesh grid defined by a plurality of mesh nodes, and thenodal motion estimator 320 is similarly configured to repeat, at each ofa plurality of mesh resolution levels, the act of estimating the nodalmotion vector for each mesh node. In this embodiment, the motion fieldinterpolator interpolates from nodal motion vectors that have beendetermined at the final mesh resolution level.

In one embodiment, the mesh nodes at each mesh resolution level afterthe first level include both previously existing nodes, and nodes thatare newly added on, at the current level. In this embodiment, the nodalmotion estimator 320 is configured to pass on, at each mesh resolutionlevel, one or more nodal motion vectors that have been estimated at thecurrent mesh resolution level, onto a subsequent mesh resolution level.In particular, for the previously existing nodes, the nodal motionestimator 320 uses the nodal motion vectors that were estimated during aprevious mesh resolution level, and that were passed onto the currentlevel from the previous level. The nodal motion estimator 320 includesan interpolator 321, which interpolates from the nodal motion vectorsestimated for the previously existing nodes, in order to estimate thenodal motion vectors for the newly added nodes. The nodal motionestimator 320 further includes a nodal motion refiner 323. For bothpreviously existing nodes and newly added nodes, the nodal motionrefiner 323 refines the nodal motion vector that has been estimated foreach node.

In one embodiment, the nodal motion refiner 323 refines the nodal motionvector by performing multi-level block matching, using apattern-intensity based similarity measure. In this embodiment, thenodal motion refiner 323 defines a block centered on each mesh node inthe DRR, and searches for a matching mesh node in the x-ray projectionimage that maximizes a similarity measure between the block in the DRR,and a matching block in the x-ray image that is centered on the matchingmesh node. In one embodiment, the similarity measure is given byequations (5) and (6) above. The nodal motion refiner 323 then refinesand modifies the nodal motion vector that was estimated for theparticular mesh node, until the nodal motion vector describes a mappingof the mesh node onto the matching mesh node.

In one embodiment, the nodal motion refiner 323 performs multi-levelblock matching, i.e. repeats for a plurality of image resolution levelsthe acts of defining a block centered on the mesh node of interest,searching for the matching mesh node, and refining the nodal motionvector. In one embodiment, the nodal motion refiner 323 defines a searchwindow around the mesh node of interest, and searches within the searchwindow for a maximum in the similarity measure. Because several localmaximums may exist, in addition to the desired global maximum, the nodalmotion refiner 323 preferably reviews a plurality of candidates whensearching for the location of the maximum in the similarity measure.

In one embodiment, the nodal motion estimator 320 is configured toestimate a global translation of the DRR, and to use the globaltranslation as an estimate for the nodal motion vector for each meshnode in the first mesh resolution level. The global translationrepresents a translation of the image center of the DRR.

In one embodiment, the apparatus 300 further includes a motion fieldreconstructor 328. The motion field reconstructor 328 is configured toreconstruct the nodal motion vector for any mesh node at whichmismatching occurs, i.e. for which the estimated nodal motion vector isunreliable. The motion field reconstructor 328 reconstructs suchunreliable nodal motion vectors by interpolating from the mesh nodesthat surround the unreliable mesh node. In this embodiment, the nodalmotion estimator 320 computes the difference between the nodal motionvector for a mesh node, and the median nodal motion vector computed fromits surrounding 9 nodes. If the difference is less than a predefinedthreshold, the node is considered as a reliable node, otherwise it isconsidered as an unreliable node. The nodal motion vector for anunreliable node is replaced with the median values, and passed on to thesubsequent mesh.

In one embodiment, the motion field reconstructor 328 reconstructs nodalmotion vectors for unreliable nodes by imposing a smoothness constrainton the nodal motion vectors estimated by the nodal motion estimator 320.In one embodiment, the motion field reconstructor 328 imposes thesmoothness constraint by formulating a cost function given by equation(8) above, and minimizing the cost function by solving the system oflinear equations, as expressed in equation (9).

In one embodiment, the motion field interpolator 330 interpolates, forany desired point of interest within the ROI of the DRR, the localmotion vector for the point of interest by interpolating from the nodalmotion vectors estimated for the surrounding mesh nodes, by performingthe summation described in equations (6) and (7).

The apparatus 300 may include a computer-readable medium having storedtherein computer-readable instructions for a processor. Theseinstructions, when read and implemented by the processor, cause theprocessor to: 1) input and store, for a first image of an object, datarepresentative of a mesh grid having a plurality of mesh nodes, for eachof a plurality of mesh resolution levels; 2) estimate, for each meshnode in each mesh resolution level, at least one nodal motion vectorthat describes a matching of the mesh node onto a corresponding meshnode in a second image; and 3) compute a local motion vector for one ormore points of interest in the first image by interpolating from thenodal motion vectors estimated at a final mesh resolution level for themesh nodes that surround each point of interest.

The computer-readable medium may have stored therein furthercomputer-readable instructions for the processor. These furtherinstructions, when read and implemented by the processor, cause theprocessor to detect any mesh node for which a mismatch occurs betweenthe mesh node (in the first image) and its corresponding mesh node (inthe second image), and to reconstruct the nodal motion vector for thedetected mesh node by imposing a smoothness constraint. Thecomputer-readable medium may be any medium known in the art, includingbut not limited to hard disks, floppy diskettes, CD-ROMs, flash memory,and optical storage devices. The computer readable instructionsdescribed above may be provided through software that is distributedthrough the Internet.

FIG. 16 is a schematic block diagram of a system 400 for performingfiducial-less non-rigid image registration, in accordance with oneembodiment. The image registration system 400 can register at least onenear real time 2D image of an anatomical region with previouslygenerated 3D scan data of the anatomical region. The anatomical regionincludes at least one treatment target and at least one referencestructure, typically a skeletal or vertebral structure. The near realtime 2D images are generated by detecting imaging beams (e.g. x-rayimaging beams) that have known intensities, and known positions andangles relative to the anatomical region, after the beams have traversedat least a portion of the anatomical region. The system 400 alsoincludes an x-ray imaging system 435 that generates imaging beams havingthese known intensities and originating from these known positions andangles.

The system 400 includes means 405 for providing the 3D scan data of theanatomical region. The 3D scan data may be CT scan data provided by a CTscanner. Alternatively, MRI scan data, provided by an MRI system, may beused. Alternatively, PET scan data, provided by a PET system, may beused. In these different embodiments, the means 305 for providing 3Dscan data may include, but is not limited to, a CT scanner, an MRIsystem, and a PET system, respectively. The system 400 includes a DRRgenerator 410 configured to generate at least one DRR (digitallyreconstructed radiograph) of the anatomical region, using the 3D scandata and the known locations, angles, and intensities of the imagingbeams.

The system 400 further includes: 1) an ROI selector 420 configured toselect an ROI (region of interest) within the DRR, the ROI containingthe treatment target and preferably at least one reference structure; 2)an image enhancer 430 configured to enhance the DRRs and the x-rayimages by applying a filter operator to the DRR and to the x-ray image;3) a similarity measure calculator 440 configured to determine a measureof similarity between the DRR and the x-ray image; 4) a motion fieldgenerator 450 configured to generate a 3D full motion field byestimating, for each of a plurality of resolution levels, one or more 2Dlocal motion fields within the ROI, using the similarity measure; and 5)a parameter determiner 460 configured to determine a set of non-rigidtransformation parameters that represent the difference in the positionand orientation of the treatment target as shown in the x-ray image, ascompared to the position and orientation of the treatment target asshown in the DRR, from the 3D full motion field.

In an embodiment in which CT data are used, the 3D scan data consist ofa plurality of CT numbers representing the image intensity ofcorresponding 3D CT voxels, where each CT voxel represents acorresponding volume element of the anatomical region, and each CTnumber represents the attenuated intensity of an x-ray CT beam that hasbeen generated at a CT scan energy level and that has traversed thecorresponding volume element of the anatomical region.

The system 400 further includes a scan data modifier 470, configured tomodify the 3D scan data before the 3D scan data are used by the DRRgenerator 410, so as to compensate for the difference between the ratioof bone-to-tissue attenuation at the CT scan energy level, and the ratioof bone attenuation at the x-ray projection energy level, i.e. at theknown intensity level of the imaging beam. The scan data modifier 470includes a processor for performing on each CT number a mathematicaloperation derived from a non x-ray attenuation model, where themathematical operation is given by:C(x,y,z)=aC ₀(x,y,z)e ^(bC0(x,y,z))In this formula, C(x,y,z) represents the modified CT number of a 3D CTvoxel having a location (x,y,z); a and b represent weightingcoefficients; and C₀(x,y,z) represents the unmodified CT number, basedon a linear attenuation model, of a 3D CT voxel having a location(x,y,z).

In one embodiment, the DRR generator 410 includes: 1) a ray castingsubsystem 412 configured to cast a plurality of hypothetical raysthrough the 3D CT voxels, at the known intensities and from the knownpositions and angles; 2) a CT number integrator 414 for integratingalong each hypothetical ray the CT numbers corresponding to the CTvoxels that are traversed by the hypothetical ray; 3) a projector 416for projecting the integrated values of the CT numbers onto an imagingplane.

In one embodiment, the CT number integrator 414 includes a bi-linearinterpolator 416 configured to perform bi-linear interpolation on thevoxels encountered by each ray, and a polynomial interpolator 418configured to perform, for each voxel of interest within a voxel slice,a one-dimensional polynomial interpolation over the voxel of interestand over voxels on each adjacent voxel slice. Bi-linear interpolation,as well as 1-D polynomial interpolation, are well known, and standardsoftware and/or algorithms that are commercially available may be used.

In one embodiment, the filter operator applied by the image enhancer 430is a top-hat filter, configured to select the pixel having the brightestpixel value from each of at least two different neighborhoods within theDRR and the x-ray image, and to eliminate the remaining pixels in theneighborhoods. Mathematically, the top hat filter is defined by equation(2) above.

In one embodiment, the ROI selector includes an entropy calculator thatcalculates a modified Shannon entropy of the DRR. The modified Shannonentropy is given by: H=−Σ_(I)IP(I) log P(I), where I is the value of theintensity of the image, at each pixel of the image, and P(I) is theprobability of an image intensity value I occurring within the ROI. TheROI selector further includes region selecting processor for selectingthe ROI so that the entropy measure H is maximized within the ROI.Calculation of Shannon entropy is well known in signal processing. Alsowell known is maximizing (or minimizing) an entropy function, fordesired purposes. Therefore, standard software and/or algorithms thatare commercially available may be used in the ROI selector 420, withonly minimal trivial revisions to incorporate the modification indicatedin the formula for modified Shannon entropy.

In one embodiment, the similarity measure calculator 440 is configuredto form a difference image by subtracting a corresponding pixel value ofthe DRR from each pixel value of the near real time (or “live”) x-rayimage, so that the pixel value at the i-th row and j-th column of thearray of pixel values for the difference image is given by:I _(diff)(i,j)=I _(Live)(i,j)−I _(DRR)(i,j).The similarity measure calculator 440 is also configured to apply uponeach pixel of the difference image a pattern intensity function, definedby summing asymptotic functions of the gradients of the difference imageover the pixels within a neighborhood R. R is defined so that thegradients of the difference image can be considered in at least fourdirections: a) a substantially horizontal direction; b) a substantiallyvertical direction; c) a substantially diagonal direction at about 45degrees; and d) a substantially diagonal direction at about −45 degrees.The pattern intensity function is characterized by a mathematicalformulation given by equations (5) and (6) above.

The details of the motion field generator 450 have been fully describedabove in conjunction with FIG. 15. In one embodiment, the parameterdeterminer 460 is configured to determine a separate set oftransformation parameters for each of pair of orthogonal projectionx-ray image, formed by projecting the anatomical region onto respectiveprojection image planes. In this embodiment, the non-rigidtransformation parameters include three translational parameters (x, y,and z), and three rotational parameters (r, p, w), where (x, y, and z)represent the three translations of the treatment target along thedirections of three mutually orthogonal x-, y-, and z-axes,respectively; and where (r, p, w) represent the three rotations (roll,pitch, yaw) of the treatment target about the three mutually orthogonalx-, y-, and z-axes. The parameter determiner 460 is further configuredto combine the resulting parameters for each projection to obtain the 3Dtransformation parameters. The 3D transformation parameters are relatedto the transformation parameters for projections A and B by thefollowing relationship:x=(x _(A) +x _(B))/2,y=(y _(A) −y _(B))/√{square root over (2)},z=(y_(A) +y _(B))√{square root over (2)},r=θ _(x) ,p=(θ_(B)−θ_(A))/√{square root over(2)},w=(θ_(B)+θ_(A))/√{square root over (2)}  (17)

Experiments in 2D/3D image registration, in accordance with thealgorithm illustrated in the flow chart in FIG. 1, and with the methodof motion field generation as described above, have been carried outusing patient clinical data. In one embodiment, a CT resolution of0.87×0.87×1.00 mm (256×256×300 voxels) has been used. FIG. 17Aschematically illustrates target localization between a DRR of thetarget and an x-ray image of the target, in an embodiment in which thetarget is located within the cervical region of the spine. FIG. 17Bschematically illustrates target localization between a DRR of thetarget and an x-ray image of the target, in an embodiment in which thetarget is located in the thoracic region of the spine. FIG. 17Cschematically illustrates target localization between a DRR of thetarget and an x-ray image of the target, in an embodiment in which thetarget is located in the lumbar region of the spine.

In order to evaluate the image registration algorithm, the targetregistration error (TRE) has been computed. The TRE is computed as thedifference between the displacements obtained using the fiducial-lesstracking, and the displacements using fiducial tracking:

$\begin{matrix}{{T\; R\; E} = \sqrt{\left( {{dx} - {dx}_{0}} \right)^{2} + \left( {{dy} - {dy}_{0}} \right)^{2} + \left( {{dz} - {dz}_{0}} \right)^{2}}} & (18)\end{matrix}$

FIG. 18 is a table of TRE (target registration error) values fordifferent targets located within the cervical, thoracic, and lumbarregions, in embodiments in which fiducials are kept in the CT scan. FIG.19 is a table of TRE (target registration error) values for differenttargets located within the cervical, thoracic, and lumbar regions, inembodiments in which fiducials are removed in the CT scan. As seen fromFIGS. 18 and 19, the mean for the TRE is less than 0.6, using thefiducial-less tracking method and system described in the presentpatent.

In sum, a method and system is disclosed in this patent for enhancing animage of an object so as to increase the visibility in the image of atleast one structure within the object, for example reference skeletalstructures and treatment targets in an anatomical region. In oneembodiment, a fiducial-less tracking method and system, based onnon-rigid image registration, includes a system and method for enhancingboth the DRRs (reconstructed from pre-operative scam data) and the nearreal time x-ray projection images. The image enhancement techniqueincludes constructing a top-hat filter operator, configured as aweighted combination of an opening of the image with a first structuralelement, and a closing of the image with a second structural element,and applying the top-hat filter operator to pixels in at least twodifferent neighborhoods within the image.

While the invention has been particularly shown and described withreference to specific preferred embodiments, it should be understood bythose skilled in the art that various changes in form and detail may bemade therein without departing from the spirit and scope of theinvention as defined by the appended claims.

1. A method of enhancing an image of an object so as to increase thevisibility in the image of at least one skeletal structure within theobject, wherein the image is characterized by a plurality of pixels,each pixel having an associated pixel value that represents the imageintensity of a corresponding unit volume of the object, the methodcomprising: generating a two-dimensional (2D) reconstructed image from athree-dimensional (3D) scan of the object, wherein the 2D reconstructedimage includes the skeletal structure; generating a 2D x-ray image ofthe object, wherein the 20×-ray image includes the skeletal structure;for each of the 2D reconstructed image and the 2D x-ray image a)selecting at least a first neighborhood and a second neighborhood withinthe image, b) constructing an operator configured to select, within thefirst and second neighborhoods, one or more pixels having an optimalpixel value, and to eliminate the remaining pixels in the neighborhoods;c) applying the operator to the selected neighborhoods so that only thepixels having the optimal pixel values remain in the selectedneighborhoods, and the remaining pixels in the selected neighborhoodsare eliminated to produce an enhanced image; and registering theenhanced 2D x-ray image with the enhanced 2D reconstructed image byderiving a transformation between a coordinate system of the enhanced 2Dxray image and a different coordinate system of the enhanced 2Dreconstructed image.
 2. A method in accordance with claim 1, wherein thefirst and the second neighborhoods are characterized by different sizes.3. A method in accordance with claim 2, further comprising the act ofconstructing the operator so that the operator is configured to performthe following acts: 1) select, from each neighborhood, the pixel havingthe largest pixel value; 2) compare the pixel selected from the smallerof the first and the second neighborhood with the pixel selected fromthe larger of the first and the second neighborhood; and 3) if the pixelselected from the smaller neighborhood is greater than the pixelselected from the larger neighborhood by a threshold amount, keep thepixel; otherwise, eliminate the pixel.
 4. A method in accordance withclaim 1, wherein the operator comprises a non-linear operator.
 5. Amethod in accordance with claim 4, wherein the operator comprises anon-linear top-hat filter operator.
 6. A method in accordance with claim1, wherein the act of selecting the neighborhoods comprises: selectingthe at least two different neighborhoods within the image so that atleast a portion of the structure within the object is included in atleast one of the neighborhoods.
 7. The method of claim 1, wherein the 2Dreconstructed image from a 3D scan is a digitally reconstructedradiograph from a computed tomography scan.
 8. An apparatus forenhancing an image of an object so as to increase the visibility in theimage of at least one skeletal structure within the object, the imagebeing characterized by a plurality of pixels, each pixel having anassociated pixel value that represents the image intensity of acorresponding unit volume of the object, the apparatus comprising: a) aneighborhood selector configured to select at least a first neighborhoodand a second neighborhood within the image for each of a two-dimensional(20) reconstructed image from a three-dimensional (3D) scan of theobject and a 2D x-ray image of the object, wherein each image of theobject includes the skeletal structure; b) an operator generator toconstruct an operator configured to select, within the first and secondneighborhoods of each image, one or more pixels having an optimal pixelvalue, and to eliminate the remaining pixels in the selectedneighborhoods; and c) a controller to apply the operator to the selectedneighborhoods of each image so that only the pixels having the optimalpixel values remain in the selected neighborhoods, and the remainingpixels in the selected neighborhoods are eliminated to produce enhancedimages, and to register the enhanced 2D x-ray image with the enhanced 2Dreconstructed image by deriving a transformation between a coordinatesystem of the enhanced 2D x-ray image and a different coordinate systemof the enhanced 2D reconstructed image.
 9. An apparatus in accordancewith claim 8, wherein the operator comprises a non linear operator. 10.The apparatus of claim 8, wherein the 2D reconstructed image from a 3Dscan is a digitally reconstructed radiograph from a computed tomographyscan.